Secondary use of routinely collected administrative health data for epidemiologic research: Answering research questions using data collected for a different purpose
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The use of routinely collected administrative health data for research can provide unique insights to inform decision-making and, ultimately, support better public health outcomes. Yet, since these data are primarily collected to administer healthcare service delivery, challenges exist when using such data for secondary purposes, namely epidemiologic research. Many of these challenges stem from the researcher's lack of control over the quality and consistency of data collection, and - furthermore - a lessened understanding of the data being analyzed. That said, we assert that these challenges can be partly mitigated through careful, systematic use of these data in epidemiologic research. This article presents considerations derived from experiences analyzing administrative health data (e.g., healthcare practitioner billings, hospitalizations, and prescription medication data) in the Canadian province of British Columbia (population of over 5 million in 2024), though we believe the underlying principles generalize beyond this region. Key considerations were organized around four themes: 1) Know the data and their primary use (understand their scope and limitations); 2) Understand classification and coding systems (appreciate the nuances regarding classification systems, versions, how they are employed in the primary uses of the data, and querying the values); 3) Transform data into meaningful forms (process data and apply identification algorithms, when necessary); 4) Recognize the importance of validity when defining analytic variables (make meaningful inferences based on data/algorithms). Although this article is not an exhaustive list of all considerations, we believe that it will provide pragmatic insights for those interested in leveraging administrative health data for epidemiologic research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it